Sparse single-hidden layer feedforward network for mapping natural language questions to sql queries

Issam H. Laradji, Lahouari Ghouti, Faisal Saleh, Musab A. Alturki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Mapping natural language (NL) statements into SQL queries allows users to interact with systems through everyday language. Semantic parsing has seen a growing interest over the past decades. In this paper, we extend single hidden layer feedforward network (SLFN) by adding the Kullback-Liebler (KL) divergence parameter to its objective function. We refer to this algorithm as Sparse SLFN (S-SLFN) which can learn whether an SQL query answers a particular NL question. With Bag of Words (BoW) representing the questions and the queries, the algorithm, by enforcing sparsity, is meant to retain robust features representing informative relationships and structure of the data. Experimental results show that S-SLFN outperforms SLFN and other algorithms for the GeoQueries dataset by a respectable margin.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2014 - 24th International Conference on Artificial Neural Networks, Proceedings
PublisherSpringer Verlag
Pages241-248
Number of pages8
ISBN (Print)9783319111780
DOIs
StatePublished - 2014
Event24th International Conference on Artificial Neural Networks, ICANN 2014 - Hamburg, Germany
Duration: 15 Sep 201419 Sep 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8681 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Artificial Neural Networks, ICANN 2014
Country/TerritoryGermany
CityHamburg
Period15/09/1419/09/14

Keywords

  • Semantic Parsing
  • Single-hidden Layer Feedforward Network (SLFN)
  • Sparsity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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